Imaging sensor anomalous pixel column detection and calibration

ABSTRACT

An imaging sensor is signaled to capture a digital image of a dark scene. For each of the pixel columns in the image, a respective column value is computed that represents at least some of the pixels in the column. For each of the pixel columns in the image, a respective comparison is made between the respective column value of the pixel column and a reference value. A respective column score is computed, for each of the pixel columns, based on the respective comparison. An indication that identifies one or more of the pixel columns as anomalous is stored, when the respective column score of the one or more the pixel columns does not meet a criterion. Other embodiments are also described and claimed.

RELATED MATTERS

This non-provisional application claims the benefit of the earlierfiling date of provisional application No. 61/605,043, filed Feb. 29,2012.

FIELD

An embodiment of the invention is related to techniques for testing animaging sensor by finding anomalous pixel columns in a digital imageproduced by the sensor, and providing a calibration map for calibratingthe imaging sensor. Other embodiments are also described.

BACKGROUND

Solid state imaging sensors are used in a wide variety of consumerelectronics products such as smart phones, tablet computers, laptopcomputers, as well as desktop units. A typical imaging sensor is anintegrated circuit die that has an array of photo sensors and ispositioned at a focal plane of an optical (lens) system that is to beaimed at a scene to be captured. When a user signals a shutter release,namely that a picture is to be taken, sensor controller circuitry willreset the sensor array and then allow it to respond to incident lightfrom the scene. A conventional higher resolution imaging sensor hasmillions of photo sensors, and relatively complex analog signalconditioning and routing circuitry that serves to collect all of thephoto sensor analog signals and convert them into digital form (usinganalog-to-digital conversion circuitry, ADC). To make this collectionand conversion process more efficient, one or more columns of photosensors are wired to share the same analog signal conditioning andmultiplexing circuitry, and also the same ADC (generally referred to ascolumn circuitry). In such a column-based approach, manufacturingvariations from one column circuit to another will cause the resultingdigitized image to exhibit vertical streaks (either bright or darkstreaks), commonly referred to as vertical fixed pattern noise, VFPN, orcolumn noise, even though the photo sensors may all be exposed to thesame, uniform incident light. This, of course, degrades the quality ofthe digital image and therefore has received the attention of imagingsystem developers.

The noise or offset exhibited by a given column may detected bycomputing the difference between an average photo sensor signal from onecolumn and an ideal or expected value. This measure of the offset isthen stored in non-volatile memory as an offset value, and may then beused to calibrate the imaging sensor by subtracting the offset valuefrom the pixel column accordingly (each time the imaging sensor is usedto take a picture).

SUMMARY

Conventional techniques for testing imaging sensors are able to detectrelatively high VFPN levels, i.e. greater than one equivalent pixelelectron (“e−”). Such techniques, however, are not able to reliablydetect substantially lower levels of column noise. It has been foundthat when certain image processing techniques are used to enhance a rawdigital image from the imaging sensor, e.g. to yield a digital imagethat is more suitable for use by a bit map graphics editor, forprinting, or for display by for example, by a typical web browser,column noise levels of less than one e− may still be disturbing to theuser. Visible bright streaks appear when such post-processed digitalimages were taken under dim scene lighting conditions. Accordingly, atechnique is needed that can detect column noise in the range fromapproximately 0.1 e− to 1.0 e−. Note that in typical testing procedures,spatially and temporally random read noise levels of about 3 e− may betolerated by the human visual system. However, since the latter is moresensitive to row and column features, rather than randomly occurringnoise, the permissible column noise needs to be several times lower thanthe random read noise.

An embodiment of the invention is a method for finding an anomalouspixel column produced by an imaging sensor, as follows. The imagingsensor is signaled to capture a digital image of a dark scene. In oneembodiment, the scene is dark in that it has essentially no visiblelight and no infrared light that would produce a substantial responsefrom the sensor. The scene is dark in that the resulting digital imageshould have no pixel intensity values that are greater than about 10% offull scale (or maximum digital number), except for those in anyanomalous pixel column. That is because the particular anomaly that isto be detected here is a bright column, which is not likely to beapparent to a human being when the scene is considered bright. The darkscene as used here may be completely dark such as when a mechanicalshutter of a camera module containing the imaging sensor is completelyclosed, or the camera lens is completely blocked off; alternatively, theimaging sensor may be placed in a dark chamber.

In addition to the dark scene, to enable a relatively low detectionthreshold as desired here, the imaging sensor needs to be configuredwith a suitable exposure setting. Thus, both an analog gain (that isapplied to photo sensor signals prior to A/D conversion) and photosensor integration time may be set to be in the high end of theiravailable ranges, for example between 75% and 100% of full scale. Such acombination of the dark scene and the correct exposure setting may bedesigned to allow a detection threshold for column noise of less thanone equivalent pixel electron, such as between approximately 0.1 e− to1.0 e−.

Once the image of the dark scene has been captured, the method continueswith computing a respective column value that represents at least some,if not all, of the pixel cells in each column of the image. For each ofthe columns, one or more respective comparisons are then made betweenthe respective column value and one or more reference values. Thereference values may be the respective column values of several adjacentneighbor columns. A respective column noise score is then computed foreach of the columns, based on the comparisons. A calibration map is thenstored (in non-volatile storage) that identifies which of the pixelcolumns are anomalous, i.e. their column noise scores do not meet apredetermined criterion.

It has been found that there may be a small amount of clipping at verylow photo sensor signal levels, such as those produced when the imagingsensor is under a dark scene. This may be due to lag (charge trapping inthe photo sensor) or analog-to-digital conversion offset issues, as wellas other difficulties in analog signal conditioning (by the columncircuits). The relatively low levels of column noise sought to bedetected here may not be observable in the presence of such clipping.Such clipping, however, may be prevented by adding a small amount ofbias signal, to the output of the photo sensor. Of course, if this biassignal is too large, for instance due to too much light being introducedinto an otherwise dark scene, the shot noise associated with the photosensor signal will make the column noise once again unobservable. A verylow level of bias has thus been found to be appropriate, on the order ofabout 1 e−. This may translate into at least a 0.5 digital number (DN),at the output of the analog-to-digital conversion process. In accordancewith an embodiment of the invention, the bias signal is created in theform of additional dark current, that is charge that has been producedin the photo sensor in the absence of light (dark scene), by adjustingthe two parameters of integration time and imaging sensor temperatureduring exposure, to obtain between 1 e− and 2 e− bias in the photosensor signal.

The above summary does not include an exhaustive list of all aspects ofthe present invention. It is contemplated that the invention includesall systems and methods that can be practiced from all suitablecombinations of the various aspects summarized above, as well as thosedisclosed in the Detailed Description below and particularly pointed outin the claims filed with the application. Such combinations haveparticular advantages not specifically recited in the above summary.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention are illustrated by way of example andnot by way of limitation in the figures of the accompanying drawings inwhich like references indicate similar elements. It should be noted thatreferences to “an” or “one” embodiment of the invention in thisdisclosure are not necessarily to the same embodiment, and they mean atleast one.

FIG. 1 is a block diagram of a system for testing an imaging sensor.

FIG. 2 is a block diagram of an example color imaging sensor.

FIG. 3 is a block diagram of an electronics device having a camerafunction and a calibrator.

FIG. 4 illustrates one instance in which the imaging sensor may betested.

FIG. 5 shows another instance of testing the imaging sensor.

FIG. 6 shows yet another instance in which the imaging sensor may betested.

DETAILED DESCRIPTION

Several embodiments of the invention with reference to the appendeddrawings are now explained. While numerous details are set forth, it isunderstood that some embodiments of the invention may be practicedwithout these details. In other instances, well-known circuits,structures, and techniques have not been shown in detail so as not toobscure the understanding of this description.

An embodiment of the invention is a process for testing imaging sensors.The process may detect multiple anomalous pixel columns, or it maydetect none at all. A threshold can be set for the maximum number ofanomalous columns, such that if too many anomalous columns are detected,the imaging sensor is marked as a fail, that is, do not ship. If fewerthan the number of threshold columns are found to be anomalous, then theimaging sensor may pass the test, however, a calibration procedure maybe needed before a picture taken using the imaging sensor is ready forthe user during in-the-field use. In that case, the test continues withstoring calibration data or a calibration map that identifies theanomalous columns, and such calibration data may be stored withinnon-volatile data storage that is shipped or associated together withthe imaging sensor. A calibration routine is then run duringin-the-field use of the imaging sensor, which reads the non-volatiledata storage and makes corrections to, in one instance, all of the pixelvalues of an identified anomalous pixel column.

The process may be part of verification testing that is performed in ahigh volume manufacture setting, e.g. at the wafer level for an imagingsensor die, at a camera module level in which the imaging sensor die hasbeen integrated or installed together with imaging optical components,or at a system level where the camera module or imaging sensor die hasbeen installed into a consumer electronics product such as a smartphone, a tablet computer, a laptop computer, a desktop computer, or ahome entertainment system.

FIG. 1 shows a system for testing an imaging sensor 2 in accordance withan embodiment of the invention. A dark scene is created for the imagingsensor 2, by, for example, closing a mechanical shutter of a cameradevice in which the imaging sensor is integrated, or by simply coveringthe camera lens. In another embodiment, the imaging sensor 2 may stillbe part of an integrated circuit semiconductor wafer, and as such thewafer itself may be placed in a dark chamber. It should therefore benoted that the test system depicted in FIG. 1 is applicable to variousinstances in which the imaging sensor 2 may find itself, and severalexamples of these are given in FIGS. 4-6 described below. Note that theterm “dark scene” refers to essentially no light source being represent.The scene may be dark in that the digital image 3 that is produced bythe imaging sensor (as a capture of the dark scene) has no pixelintensity values that are greater than 10% of the full scale or maximumdigital number (DN), except for those in an anomalous pixel column. Asexplained above, a dark scene is needed because the anomaly that isbeing detected here, namely a brighter column, is either not likely tobe apparent to a human being or is simply absent due to changes in thephoto sensor signal conditioning path, when the captured scene is brightor even dimly lit.

The system also includes a camera exposure control unit 4 that signalsthe imaging sensor 2 to capture the digital image 3 of the dark scene.This signaling includes configuring the appropriate exposure settinginto the imaging sensor 2, including, in particular, the parameters ofintegration time and photo sensor signal path analog gain, e.g., bywriting binary values into appropriate configuration or controlregisters of the sensor 2. Suggestions on setting these two parametersfor advantageous results will be given below. The system also includes anoise score calculator 5 that is to compute a separate noise score foreach of the pixel columns that have been selected in the image 3. Anexample of how to calculate the noise score is given below. The noisescore calculator 5 produces a noise score array 6, which contains aseparate noise score for each of the selected pixel columns. Adiscriminator unit 7 is also provided that is able to find which ones ofthe pixel columns have noise scores that do not meet a predeterminedcriterion and are therefore anomalous columns. The discriminator unit 7identifies the anomalous columns by producing a list of pixel columnsthat do not meet the noise score criterion. A decision unit 9 has aprogrammable pass/fail threshold which may be a column count that isused to indicate whether the imaging sensor 2 has or has not passed thisquality test. When the number of anomalous columns identified by thediscriminator unit 7 is greater than the programmable threshold columncount, then the imaging sensor 2 has not passed the quality test. If ananomalous column is not found by the discriminator unit 7, then theimaging sensor 2 may be deemed to have passed the test (withoutqualifications). On the other hand, if at least one anomalous column wasfound but fewer than the threshold count, then the imaging sensor 2 isstill considered to pass the test, however, a calibration procedure mayneed to be performed each time a picture is to be taken using the sensor2.

Turning now to FIG. 2, an example of the imaging sensor 2 is shown thathas a Bayer-type color filter array or mosaic. In this embodiment, theimaging sensor 2 has an array of photo sensors where each is tuned tosense a single color, in this case, one of the three primary or additivecolors of red, green, and blue. Of course, the testing techniquesdescribed here will also work for other color imaging sensors havingdifferent color filter mosaics. In general, such sensors may beimplemented using various fabrication techniques, although acomplimentary metal oxide semiconductor (CMOS) process is particularlyuseful as it enables lower cost integration of timing, control and imageprocessing logic onto the same die as the array of photo sensors.

As depicted in FIG. 2, the control logic may include a row addresscircuit 10 that is responsible for resetting and controlling the photosensors, so as to allow them to respond to incident light in accordancewith a selected integration time. As part of the exposure setting, ananalog signal conditioning block is configured. This block applies ananalog gain to the photo sensor signal path, somewhere between the photosensor and an analog-to-digital (A/D) conversion block 11. There areseveral different architectures possible for the analog signal pathbetween the individual photo sensors and the A/D conversion circuitry11, and these are generally depicted here by analog signal conditioningblocks and an analog multiplexor block. In some cases, there is aseparate dedicated analog amplifier for each column. However, in otherinstances, multiple columns share a single analog amplifier. As yetanother alternative, multiple columns may be multiplexed by an analogmultiplexor block, and the analog gain is actually applied after ordownstream of the multiplexor block but upstream of the A/D conversioncircuitry 11. The latter produces a digital raw image, i.e. one that hasbeen minimally processed and that will need to be further enhanced bybeing subjected to image processing operations such as de-mosaicing,white balance and gamma correction for example, before being useful to auser.

Referring back to FIG. 1, the image 3 produced by the imaging sensor 2may in some cases be separated into multiple color planes, where in thisexample there may be four color planes such as those corresponding tothe red, green1, green2, and blue channels of a conventional Bayerpattern (see FIG. 2). The noise score calculator 5 would then operate oneach color plane separately, in computing a separate noise score foreach of the pixel columns in each of the color planes. This results in aseparate noise score array 6 being created, one for each color plane.Further, the discriminator unit 7 when identifying columns whose noisescore does not meet a criterion would do so for each of the color planesseparately. In addition, the decision unit 9 may render its faildecision merely based on finding that there are too many anomalouscolumns in a single color plane. An example of a quality test processthat can be implemented by the components of the system shown in FIG. 1will be described next.

A process for finding an anomalous pixel column produced by the imagingsensor 2 may proceed as follows. The imaging sensor 2 is signaled tocapture a digital image of a dark scene, where the image should becaptured at full sensor resolution. In addition, the raw digital image 3should not be cropped. As was suggested above, the integration time andanalog gain parameters should be set so as to provide a bias signal tothe photo sensor cells, where this should also be selected in view ofthe current temperature of the imaging sensor. Note that dark currentaccumulation within a photo sensor, in the case of a CMOS sensor, varieswith the temperature of the sensor, roughly doubling every 5-7 degreescentigrade; it also varies linearly with integration time. For instance,at about 16× analog gain, about 133 msec. integration time, and a sensortemperature of about 50 degrees centigrade, such conditions produceabout 1.5 e− of bias, assuming a dark current level of about 100e−/second. More generally, both the integration time and the analog gainmay be set to fall within, for example, an upper 25% of the availablerange for each, when the sensor is at about 50 degrees centigrade. Inthis way, the clipping effects mentioned above may be reduced or eveneliminated.

The following image processing operations may then be performed on theraw image in order to calculate a noise score for each column (alsoreferred to here as a VFPN score or a column score). Such operations maybe performed by the noise score calculator 5, which may be implementedas a hardwired logic circuit or alternatively as a programmed processor.Operation begins with separating the raw image frame into itsconstituent color planes. In the case of a Bayer color filter array ormosaic, assuming the imaging sensor has a full resolution of 2604×1952photosensors, there are four color planes each having about 1302×976pixels. This is just an example of course, as other color filter mosaicswith different imaging sensor resolutions will result in a fewer orgreater number of color planes and also different color plane sizes.

Next, operation proceeds with, in each color plane, computing arespective column value that represents at least some of the pixels inthe column. The respective column value may be a measure of the centraltendency of the column. For example, the measure of the central tendencymay be the average of all pixels in the column. This results in aone-dimensional array of, in this case, 1302 column values beingcomputed, for each color plane.

Next, operation proceeds with calculating a column score or alsoreferred to as a VFPN or noise score for each column. In one embodiment,differences between the measure of the central tendency of the columnand those of two or more neighbor columns, respectively, are computed;these differences are then combined to form the noise score for thecolumn. For example, the VFPN score for each column may be calculated asfollows:

-   a. Subtract the column (n−2) average from the current column (n)    average;-   b. Subtract the column (n−1) average from the current column (n)    average;-   c. Subtract the column (n+1) average from the current column (n)    average;-   d. Subtract the column (n+2) average from the current column (n)    average; and    Sum the four differences computed in a-d, take the absolute value of    the sum, and then store this value as the VFPN score for the current    column (n).

Note that in some cases, the VFPN score for the first few columns andthe last few columns (in each color channel) may be set to zero, becausesuch columns may be considered “don't care” columns. In other words,those columns even if anomalous may not be apparent to a human userviewing the image. It should also be noted that while in the aboveexample the comparison between the column value of a current column (n)and that of its neighbors involved computing the difference betweentheir respective column values, an alternative may be to compare thecolumn value to a reference value such as an expected value.

Once the noise score arrays 6 have been computed as above, thediscriminator unit 7 (see FIG. 1) may proceed with creating a list ofall pixel columns (in each color channel) that exceed a predeterminedVFPN threshold or noise score criterion. The following should be notedregarding this detection threshold. As was suggested above, a detectionthreshold in the range of approximately 0.1 e− to 1.0 e− may be expectedof the quality test described here. At the above-specified exampleoperating conditions of approximately 16× analog gain, about 133 msecintegration time, and assuming a pixel charge capacity (in the photosensor) of about 4,100 electrons, the full scale range would be about256 e−, corresponding to an ADC step size of about 0.25 e−/DN (electronsper ADC bin). An isolated 2 DN offset in a single column (which may beequivalent to about 0.5 e− photo sensor column noise level) would resultin a noise score of 8; this may be adopted as the noise score criterionor threshold (in one embodiment). The discriminator unit 7 so configuredwill produce a list of all columns whose noise score exceeds the noisescore criterion of, in this example, 8.

The testing process continues with making a decision (via a decisionunit 9) as to whether the imaging sensor 2 has passed the test. If thenumber of columns found to exceed the detection threshold is excessive,then the imaging sensor fails the test. Of course, if there are nocolumns whose noise scores exceed the detection threshold, then theimaging sensor may pass without qualifications. On the other hand, theimaging sensor may pass this quality test by having at least one, butfewer than the maximum number of anomalous column; in that case,however, calibration of the imaging sensor may be needed, when taking apicture subsequently during in-the-field use of the sensor 2. This isdescribed below in connection with FIG. 3.

In a further embodiment, the decision unit 9 will indicate that theimaging sensor 2 has failed the quality test when, within a single colorplane, there are two or more anomalous columns found that are adjacent.Thus, even if there are fewer than the maximum number of anomalouscolumns, the imaging sensor 2 may still be considered to fail thequality test if there are two or more anomalous columns that areadjacent, that is their index values differ by only one. Other noisescore criteria may alternatively be used.

As part of a calibration setup procedure, that is performed upon findingat least one anomalous pixel column, an indication that identifies oneor more pixel columns as being anomalous is stored. This may be done bywriting the index of the anomalous column to a non-volatile data storagedevice, and then associating the index value with the serial number orother identifying characteristic of the imaging senor 2. Thisinformation may be written as part of a calibration map 16 such as theone depicted in FIG. 3. The calibration map 16 may be stored innon-volatile storage 15, which may be any type of memory or registerfile storage contained in a consumer electronics device in which thesensor 2 has been integrated.

FIG. 3 shows a system having a digital camera function that cancalibrate its imaging sensor 2. The non-volatile storage 15 haspreviously stored data therein, which in this case includes thecalibration map 15 that was written as a result of the anomalous columndetection process described above. In a broad sense, the data includesan index of at least one anomalous pixel column that has been previouslydetected for the imaging sensor 2. In this example, there are severalsuch indices, pointing to several anomalous pixel columns, up to amaximum of 8 (that being the upper limit on the number of anomalouspixel columns that a passing imaging sensor is allowed to have). Acalibrator circuit 17, which is coupled to the imaging sensor 2 and thenon-volatile storage 15, is to correct a pixel column that is at theindex.

The calibration map 16 may contain an Enable bit, which when setindicates that column noise calibration data has been programmed intothis module or system. The numCols field indicates the number of columnsthat are to be calibrated (which cannot be greater than the maximumanomalous column count). The Location field indicates the location wherethe anomalous pixel column detection and calibration setup wasperformed, e.g. imaging sensor supplier, camera module integrator, orsystem integrator). The Version field indicates which version of thecalibration map 15 this is.

The calibration map 16 identifies each anomalous column by indicatingwhich color channel the column is in, e.g. 0=B, 1=Gb, 2=Gr, 3=R, in aColCh field, and by indicating the index of the column which may be anumber in the range 0 to N (see FIG. 1). The column having the highestnoise score is reserved at ColCh0, ColIndex0, and the actual noise scoreof that column is written into the Intensity field.

A method for calibrating the imaging sensor 2 may proceed as follows. Acurrent digital raw image is about to be produced by the imaging sensor2. The calibrator 17 reads a reserved location for the Enable field inthe non-volatile storage 15 and finds that it is set, which means thatcalibration of the imaging sensor 2 is needed. It then continues withreading ColCh0 and ColIndex0, which identify the color plane and theindex, within a digital raw image, of an anomalous pixel column that hadthe highest noise score during previous testing. The calibrator 17 mayread any additional ColCh and ColIndex fields, to identify the rest ofthe anomalous pixel columns. It then proceeds with correcting theidentified pixel columns in the current digital raw image. Thecorrection may include replacing an intensity value of each pixel in anidentified column with a combination of the intensity values of one ormore neighboring adjacent pixels in the same color plane (e.g., anaverage of a left neighbor pixel and a right neighbor pixel).

In one embodiment, the calibration method determines a measure of thescene-wide lighting that was present when the current digital raw imagewas captured. The correction to an identified pixel column is onlyperformed when the measure of scene-wide lighting is dim, but not whenthe measure of scene-wide lighting is bright. That is because thecalibration may not be needed for bright scenes, because as wasdiscussed earlier the bright vertical streaks are not apparent or areabsent under those conditions. One advantageous way for determining ameasure of scene-wide lighting involves determining the analog gain thatwas applied to a photo sensor signal path in the imaging sensor, whilethe current image was being captured. The determined measure ofscene-wide lighting is dim when the analog gain is high, and bright whenthe analog gain is low.

The column noise quality testing procedures described above are expectedto be most useful in a high volume manufacture production line test,such as wafer level testing of imaging sensors (see FIG. 4), testing bya camera module integrator (FIG. 5), or testing by a system integratorof a consumer electronic product (FIG. 6). The quality testing processmay alternatively be performed during in-the-field use of the consumerelectronic product, in a way that is transparent to the user. Inparticular, the elements of the test system shown in FIG. 1 includingthe camera exposure control unit 4, noise score calculator 5,discriminator unit 7, and decision unit 9 may all be implemented bysuitably programming, for instance, an applications processor or asystem on a chip of the consumer electronic product, as a form of abuilt-in self-test for the camera function. The test process in thatcase may be triggered automatically when software detects that thecamera lens is entirely blocked off from light, e.g. an essentiallyblack image is produced by the imaging sensor; this may signify that theimaging sensor is exposed to a dark field such that the test may beperformed. The results of the test, including identification ofanomalous columns, may then be automatically reported to a manufactureror distributor of the consumer electronic product, by sending a messageover the Internet to a remote server that provides a report on thehealth of the consumer electronic product.

As explained above, an embodiment of the invention may be amachine-readable medium (such as microelectronic memory) having storedthereon instructions, which program one or more data processingcomponents (generically referred to here as a “processor”) to performthe digital image processing operations described above including noiseand signal strength measurement, filtering, mixing, adding, inversion,comparisons, and decision making. In other embodiments, some of theseoperations might be performed by specific hardware components thatcontain hardwired logic (e.g., dedicated digital filter blocks). Thoseoperations might alternatively be performed by any combination ofprogrammed data processing components and fixed hardwired circuitcomponents.

While certain embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative of and not restrictive on the broad invention, andthat the invention is not limited to the specific constructions andarrangements shown and described, since various other modifications mayoccur to those of ordinary skill in the art. For example, while FIG. 1and the particular example of the VFPN test described above refer to theraw digital image 3 as being composed of multiple color planes, suchthat the noise score calculations and the identification of anomalouspixel columns occurs on a per color plane basis, there are some imagingsensors that do not have a color filter mosaic and are able to producefull color pixel values (e.g., RGB pixel values) directly at the outputof the analog-to-digital conversion circuitry 11 (see FIG. 2). Thetesting described above may also be useful for such color imagingsensors. The description is thus to be regarded as illustrative insteadof limiting.

What is claimed is:
 1. A method for finding an anomalous pixel columnproduced by an imaging sensor, comprising: signaling the imaging sensorto capture a digital image of a dark scene, wherein the digital imageincludes a plurality of pixel columns; for each of the plurality ofpixel columns, computing a respective column value that represents someof the pixels in the column; for each of the plurality of pixel columns,making a respective comparison between the respective column value ofthe pixel column and a reference value; computing a respective columnscore, for each of the plurality of pixel columns, based on therespective comparison; and storing an indication that identifies one ofthe pixel columns as anomalous when the respective column score of saidone of the pixel columns does not meet a criterion.
 2. The method ofclaim 1 wherein the dark scene has essentially no visible light and noinfrared light that would produce a substantial response from theimaging sensor.
 3. The method of claim 1 wherein the scene is dark inthat the captured digital image has essentially no pixel intensityvalues that are greater than about 10% of full scale or maximum digitalnumber, except for those in any anomalous pixel column.
 4. The method ofclaim 1 wherein computing a respective column value that represents someof the pixels in the column comprises summing all pixels in the column.5. The method of claim 1 wherein the digital image comprises a pluralityof color planes and is a raw image.
 6. The method of claim 4 wherein therespective column value is computed from the digital image being a rawimage that has been captured at full resolution of the imaging sensorand has not been cropped.
 7. The method of claim 1 wherein the imagingsensor has a color filter mosaic or array and the plurality of pixelcolumns are in a single color plane of the digital image, and therespective column value is computed from the single color plane and notother color planes of the digital image.
 8. The method of claim 4wherein the reference value comprises a respective column value of anadjacent neighbor column of the plurality of pixel columns.
 9. Themethod of claim 8 wherein the respective comparison comprises adifference computed between the respective column value of the pixelcolumn and the respective column value of the adjacent neighbor column.10. A system for testing a color imaging sensor, comprising: means forcreating a dark scene for the imaging sensor; means for signaling theimaging sensor to capture a digital image of the dark scene; means forcomputing a separate noise score for each of the pixel columns; andmeans for identifying those columns whose noise score does not meet acriterion.
 11. The system of claim 10 further comprising: means forseparating the digital image into a plurality of color planes, eachcolor plane having a plurality of pixel columns, wherein said means forcomputing is to compute a separate noise score for each of the pixelcolumns in each of the color planes, and wherein said means foridentifying is to identify those columns whose noise score does not meeta criterion, for each of the color planes.
 12. The system of claim 10further comprising: means for indicating that the imaging sensor hasfailed a quality test, when there are more than a predeterminedthreshold number of columns whose noise score does not meet thecriterion; and means for indicating that the imaging sensor has passedthe quality test but needs to be calibrated, when there are fewer thanthe predetermined threshold number of columns whose noise score does notmeet the criterion.
 13. The system of claim 10 wherein the noise scorecomputing means computes a separate measure of the central tendency ofeach of the pixel columns and computes differences between the measureof the central tendency of the column and a plurality of neighborcolumns, respectively and combines the differences to form the noisescore for the column.
 14. A system for testing a color imaging sensor,comprising: an exposure control unit that is coupled to a color imagingsensor, to configure an exposure setting and signal the sensor tocapture a raw digital image at the configured exposure setting; a noisescore calculator that is coupled to the sensor to receive the raw imageand compute a separate noise score for each of a plurality of pixelcolumns in the raw image; and a discriminator unit coupled to the noisescore calculator to find which ones of the plurality of pixel columnshave noise scores that do not meet a criterion and are deemed anomalouscolumns.
 15. The system of claim 14 further comprising: a decision unitcoupled to the discriminator unit and having a programmable threshold,wherein the decision unit is to indicate that the imaging sensor has notpassed a quality test when the number of anomalous columns found by thediscriminator unit is greater than the programmable threshold.
 16. Thesystem of claim 15 wherein the decision unit is to indicate that theimaging sensor has passed the quality test but needs to be calibrated,when the number of anomalous columns found by the discriminator unit isless than the programmable threshold.
 17. The system of claim 16 whereinthe raw image comprises a plurality of color planes, and wherein thenoise score calculator is to compute, for each of the color planes, aseparate noise score for each of a plurality of pixel columns in thecolor plane.
 18. The system of claim 17 wherein the decision unit is toindicate that the imaging sensor has not passed the quality test when,within a single color plane, there are two or more anomalous columnsfound that are adjacent.
 19. The system of claim 14 wherein the exposurecontrol unit is to set integration time and analog gain of the imagingsensor to within an upper 25% of the available range for each, when theimaging sensor is to be tested at about 50 degrees centigrade
 20. Amethod for calibrating an imaging sensor, comprising: reading fromnon-volatile storage an index of an anomalous pixel column, that hasbeen previously detected for the imaging sensor; and correcting a pixelcolumn that is at said index, wherein the pixel column is in a digitalimage that is currently captured by the imaging sensor.
 21. The methodof claim 20 further comprising: determining a measure of scene-widelighting, wherein the correction is performed when the measure ofscene-wide lighting is dim, and not when the measure of scene-widelighting is bright.
 22. The method of claim 21 wherein determining ameasure of scene-wide lighting comprises: determining the analog gainthat was applied to a photo sensor signal path in the imaging sensorwhile the image was captured, wherein the determined measure ofscene-wide lighting is dim when the analog gain is high, and thedetermined measure of scene-wide lighting is bright when the analog gainis low.
 23. The method of claim 20 wherein correcting a pixel columncomprises replacing an intensity value of each pixel in the column witha combination of the intensity values of one or more neighboring pixels.24. A system having a digital camera, comprising: an imaging sensor tocapture a current digital image a scene; a non-volatile storage devicehaving previously stored data therein, including an index of ananomalous pixel column that has been previously detected for the imagingsensor; and a calibrator coupled to the imaging sensor and thenon-volatile storage device, the calibrator is to correct a pixel columnthat is at said index, wherein the pixel column is in the currentdigital image.
 25. The system of claim 24 wherein the previously storeddata includes a plurality of indices of a plurality of anomalous pixelcolumns, respectively, that have been previously detected for theimaging sensor, and wherein the data indicates the index of, and anintensity of, the anomalous pixel column that has the highest noisescore amongst the plurality of anomalous pixel columns.
 26. The systemof claim 24 wherein the previously stored data indicates a color channelof the previously detected anomalous pixel column.
 27. The system ofclaim 26 wherein the imaging sensor has a color filter mosaic.